Large Language Models (LLMs) have shown remarkable progress in various tasks, but complex reasoning remains a challenge. Think about it: can an AI truly understand and solve a multi-step problem, like a tricky math question or a nuanced real-world scenario? New research suggests that the key to unlocking advanced reasoning in LLMs might lie in teamwork. A novel technique called Multi-Agent LLM Training (MALT) explores the power of collaboration by creating specialized AI agents that work together, much like a team of human experts. Imagine one AI generating an initial solution, another verifying its accuracy, and a third refining the answer based on feedback. This collaborative approach mimics human problem-solving strategies and allows for a more robust and nuanced approach to complex tasks. MALT utilizes a clever strategy involving three distinct LLMs: a generator, a verifier, and a refinement model. These agents engage in a back-and-forth process, generating solutions, critiquing them, and iteratively improving upon them. This process generates a rich dataset of 'reasoning trajectories' which are then used to fine-tune the individual models. The results are impressive. MALT demonstrated significant improvements on challenging reasoning benchmarks like MATH, GSM8k, and CSQA. For example, on the MATH dataset, MALT achieved a relative improvement of 14.14% over the baseline single-model approach. This suggests that collaborative training can significantly boost the reasoning capabilities of LLMs, even relatively small ones. While still in its early stages, MALT offers a promising glimpse into the future of AI reasoning. By leveraging the power of teamwork, we might be able to unlock even more sophisticated problem-solving abilities in LLMs and pave the way for truly autonomous AI agents capable of tackling real-world challenges.